Dynamic Energy Budgeting for Adaptive Computing Device Settings
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Solution Overview
Problem
Devices often consume more energy than necessary due to static device settings, leading to inefficiency, reduced lifespan, and increased heat generation, as users manually adjust settings based on anticipated maximum resource use without considering varying task requirements.
Innovation Solution
An efficiency budgeting system dynamically adjusts device settings based on computational resource requirements and energy efficiency budgets, automatically implementing and modifying settings to optimize energy use without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If device settings are statically configured for maximum resource use, then performance requirements are met, but energy consumption increases
Solution Approach 1:
The patent implements dynamic device settings that automatically adjust processing resource allocation based on real-time task requirements. The system monitors computational workload and dynamically modifies CPU frequency, GPU usage, and memory allocation to match actual performance needs, transitioning from static maximum配置 to adaptive dynamic配置, thereby reducing energy consumption while maintaining required performance levels
Solution Approach 2:
The system changes key operational parameters including CPU clock speed, graphics processing intensity, and component activation states based on task complexity. By adjusting these parameters dynamically rather than maintaining fixed maximum values, the system optimizes the balance between performance delivery and energy consumption across different operational scenarios
2Productivity
If processing resources are increased to meet maximum task requirements, then task performance is ensured, but device lifespan is reduced
Solution Approach 1:
The system implements dynamic resource management that adjusts processing intensity based on actual task demands rather than maintaining constant maximum resource utilization. This reduces cumulative stress on processing components through adaptive load management, thereby extending device operational lifespan while ensuring peak performance is available when required
Solution Approach 2:
The system proactively manages thermal and electrical stress by implementing preventive cooling strategies and load distribution mechanisms before critical thresholds are reached. This cushioning approach reduces cumulative degradation effects on processing components, extending device lifespan while maintaining performance capabilities
3Productivity
If processing resources are increased to meet maximum task requirements, then computational capability is enhanced, but heat generation increases
Solution Approach 1:
The system dynamically adjusts processing resource allocation and component activation based on real-time thermal conditions and task requirements. By modulating CPU frequency, GPU usage, and peripheral device activation according to actual computational needs, the system reduces unnecessary heat generation while maintaining required computational capability
Solution Approach 2:
The system changes operational parameters including processing frequency, component power states, and cooling system activation based on thermal conditions. These parameter adjustments optimize the balance between computational performance and heat generation, reducing thermal stress on components
4Productivity
If device settings are manually adjusted for anticipated maximum use, then performance needs are covered, but energy efficiency is compromised
Solution Approach 1:
The system implements self-service automation where the device automatically monitors its own operational state, task requirements, and energy consumption patterns. This self-managing capability eliminates the need for manual user configuration while optimizing energy efficiency through real-time adaptive adjustments to processing resources and component activation
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor task requirements, current resource utilization, and energy consumption. This feedback loop enables automatic adjustment of device settings to optimize the balance between performance coverage and energy efficiency, adapting to changing conditions without manual intervention
Data Source
AI summary
One embodiment provides a method, the method including: generating, utilizing an efficiency budgeting system, an energy efficiency budget for a computing device; automatically implementing energy consumption settings for the computing device to meet the energy efficiency budget for the computing device; and dynamically modifying, using the efficiency budgeting system, the energy consumption settings based upon computational resource requirements for the computing device requested by the user and in view of the energy efficiency budget. Other aspects are claimed and described.